ArticleJournal of the American Statistical Association2023
Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes.
Article in Journal of the American Statistical Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Adaptive Debiased Lasso in High-Dimensional Generalized Linear Models with Streaming Data.Journal of the American Statistical Association · 2026Article
- Towards a systematic framework to assess restoration success of interventions in coral reef ecosystems.PloS one · 2026Article
- Deconfounded and debiased estimation for high-dimensional linear regression under hidden confounding with application to omics data.Bioinformatics (Oxford, England) · 2025Article
- Simultaneous inference for generalized linear models with unmeasured confounders.Journal of the American Statistical Association · 2025Article
- Debiased lasso after sample splitting for estimation and inference in high-dimensional generalized linear models.The Canadian journal of statistics = Revue canadienne de statistique · 2025Article
- Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer.Journal of the American Statistical Association · 2024Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach.Journal of the American Statistical Association · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered. A two-step weighted bias-correction method is proposed for constructing confidence intervals and simultaneous hypothesis tests for individual components of the regression vector. Minimax lower bound for the expected length is established and the proposed confidence intervals are shown to be rate-optimal up to a logarithmic factor. The numerical performance of the proposed procedure is demonstrated through simulation studies and an analysis of a single cell RNA-seq data set, which yields interesting biological insights that integrate well into the current literature on the cellular immune response mechanisms as characterized by single-cell transcriptomics. The theoretical analysis provides important insights on the adaptivity of optimal confidence intervals with respect to the sparsity of the regression vector. New lower bound techniques are introduced and they can be of independent interest to solve other inference problems in high-dimensional binary GLMs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.